OpenDerm — Open-source robotic 3D skin imaging

OpenDerm

OpenDerm is an open-source robotic imaging system for creating high-resolution, reproducible 3D maps of the skin. Registered scans help reveal new lesions and identify subtle changes in existing ones.

The hardware designs, software, and build documentation are all openly available.

The Problem ↓<br>The Robot ↓<br>How it Works →<br>Scan viewer →

01 / THE PROBLEM<br>For melanoma, early detection can make the difference between a highly treatable cancer and a life-threatening one.

More than 100,000 Americans are diagnosed with melanoma each year. This form of skin cancer has a five-year relative survival rate of nearly 100% when detected early, but about 34% after it reaches distant parts of the body.1 Approximately 30% of melanomas develop within an existing mole, while 70% appear as new lesions.2 In either case, the earliest visible sign may be small: a millimeter-scale change at the edge of a mole, a localized shift in its color or structure, or a new spot only a few millimeters across. Detecting changes at this scale requires a precise record of the skin’s prior appearance.

30% of cases start from an existing mole<br>70% of cases appear as new lesions

existing mole

new melanoma

new melanoma<br>no mole here before

For people with many moles, detecting change requires tracking the entire skin surface over time. A clinician can examine the skin during an office visit, but without a standardized photographic baseline, determining whether one spot among hundreds is new or changing can be difficult. Total-body photography can provide this longitudinal record, but access to such systems remains very limited due to high costs and limited clinical evidence that existing systems reliably catch melanoma earlier than dermatologists.34

Improving total-body imaging requires progress in three areas:

01

Higher Resolution Imaging

Many existing total-body photography systems use wide fields of view to capture large areas of skin, but at the expense of fine detail. Detecting subtle changes in a lesion’s boundary, color, or internal structure requires higher-resolution images.

02

Longitudinal datasets

Most AI models for skin-cancer detection are trained on isolated images of lesions already identified as suspicious. These datasets therefore provide little evidence of how melanomas first emerge and evolve before drawing clinical attention. Training models to recognize earlier signs of melanoma will require repeated, high-resolution images of the same skin over time.

03

Frequent, accessible scanning

Total-body photography must become more affordable and widely available. Patients at high risk should be able to receive frequent, standardized scans.

A robotic imaging system offers a practical way to meet these requirements. By moving close to the body and following its contours, the system can maintain consistent distance, angle, focus, and lighting while capturing high-resolution images across the skin surface. Using a single camera with cheap actuators also reduces hardware cost compared with a large array of fixed cameras.

1. National Cancer Institute. SEER Cancer Stat Facts: Melanoma of the Skin. Estimated 2026 incidence; five-year relative survival based on SEER 21 data, 2016–2022. ↩

2. Pampena R, Kyrgidis A, Lallas A, et al. A meta-analysis of nevus-associated melanoma: Prevalence and practical implications. Journal of the American Academy of Dermatology. 2017;77(5):938–945.e4. ↩

3. Soyer HP, Jayasinghe D, Rodriguez-Acevedo AJ, et al. 3D Total-Body Photography in Patients at High Risk for Melanoma: A Randomized Clinical Trial. JAMA Dermatology. 2025;161(5):472–481. ↩

4. Lindsay D, Soyer HP, Janda M, et al. Cost-Effectiveness Analysis of 3D Total-Body Photography for People at High Risk of Melanoma. JAMA Dermatology. 2025;161(5):482–489. ↩

02 / THE HARDWARE<br>The Robot

OpenDerm is built primarily from off-the-shelf parts. The video below shows the assembled prototype running a capture sequence at 20&times; speed.

Fig. 5.0 — The assembled prototype, mid-scan (20&times;). -->

The gantry moves the camera through four degrees of freedom . Three linear axes position the sensor head within the workspace: X travels along the side rails, Y moves across the top beam, and Z sets the camera height. A rotary axis (RX ) tilts the sensor head to align the camera with the skin surface.

X Long travel<br>Y Cross travel<br>Z Vertical travel<br>RX Pan rotation<br>OpenDerm &middot; 4-DOF gantry

Fig. 5.1 — The four degrees of freedom. -->

The sensor head carries a Canon EOS R7 with an RF 100&thinsp;mm macro lens , a Godox MF-R76 ring flash fitted with a cross-polarization filter that reduces specular glare, and two downward-facing laser distance sensors . The distance measurements allow the control system to maintain a consistent working distance and camera orientation relative to the skin.

Canon EOS R7<br>Canon RF 100mm lens<br>Godox MF-R76 Flash +Cross Polarization Filter<br>&times;2 laser...